Environment-aware cross-layer communication protocol in underground oil reservoirs

ABSTRACT

Example computer-implemented methods, computer-readable media, and computer systems are described for providing communication protocol architecture or framework for magnetic induction (MI)-based communications in wireless underground sensor networks (WUSNs), for example, in underground oil reservoirs. In some aspects, environment information of an underground region that affects the transmission qualities of MI communications is evaluated. A protocol stack is identified. The protocol stack includes a number of layers for MI communications among a number of sensors in a WUSN in the underground region. A cross-layer framework and the distributed protocol are built to jointly optimize communication functionalities of the plurality of layers based on the evaluation.

TECHNICAL FIELD

This disclosure relates to communication protocols in underground oilreservoirs.

BACKGROUND

Wireless underground sensor networks (WUSNs) have attracted highattention for their great variety of novel applications, such asunderground soil condition and power grid monitoring, mine disasterprevention and rescue, oil gas extraction, earthquake and landslideforecast, border patrol and security, and many more other applications.Unlike typical wireless sensor networks that place wireless sensors interrestrial environments, WUSNs include wireless sensors in undergroundenvironments. The techniques for the typical terrestrial wireless sensornetworks may not be (or are not) directly applicable to WUSNs due to thesignificant differences between the communication media (for example,between the soil and air).

SUMMARY

This disclosure relates to communication protocols in underground oilreservoirs.

In general, example innovative aspects of the subject matter describedhere can be implemented as a computer-implemented method, implemented ina computer-readable media, or implemented in a computer system, forcommunication protocol design in underground oil reservoirs.

One computer-implemented method includes evaluating environmentinformation of an underground region that affects the transmissionqualities of magnetic induction (MI) communications; identifying aprotocol stack including a plurality of layers for MI communicationsamong a plurality of sensors in a wireless underground sensor network(WUSN) in the underground region; and building a cross-layer frameworkto jointly optimize communication functionalities of the plurality oflayers based on the evaluation.

This, and other aspects, can include one or more of the followingfeatures. The protocol stack is a three-layer protocol stack thatincludes a physical layer, a data link layer, and a network layer.

In some aspects, the cross-layer framework is realized by anenvironment-aware protocol (DEAP) process based on the evaluation. TheDEAP process includes one or more of: a distributed power control; anevaluation of a multiple access scheme for a data link layer of theprotocol stack; or a two-phase decision process for performing a routingalgorithm for a network layer of the protocol stack.

Another computer-implemented method includes identifying, by each of aplurality of sensors in a wireless underground sensor network (WUSN) inan underground region, a plurality of environment-dependent parametersmeasured by the plurality of sensors; identifying, by each of theplurality of sensors, respective communication functions for a pluralityof layers of a protocol stack for magnetic induction (MI) communicationsamong the plurality of sensors in the WUSN in the underground region;identifying, by each of the plurality of sensors, an optimizationproblem for jointly optimizing the respective communication functions ofthe plurality of layers of the protocol stack based on the plurality ofenvironment-dependent parameters, the optimization problem including aplurality of transmission parameters defining the respectivecommunication functions of the plurality of layers of the protocolstack; determining, by each of the plurality of sensors, the pluralityof transmission parameters by solving the optimization problem; andtransmitting, by each of the plurality of sensors based on magneticinduction, signals using the plurality of transmission parametersdefining the respective communication functions of the plurality oflayers of the protocol stack.

Other implementations of this aspect include corresponding computersystems, apparatuses, and computer programs recorded on one or morecomputer storage devices, each configured to perform the actions of themethods. A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of software, firmware, or hardware installedon the system that in operation causes or causes the system to performthe actions. One or more computer programs can be configured to performparticular operations or actions by virtue of including instructionsthat, when executed by data processing apparatus, cause the apparatus toperform the actions.

For example, a system comprising a WUSN that includes a plurality ofsensors in a wireless underground sensor network (WUSN) in anunderground region. Each of the sensor can include memory and dataprocessing apparatus configured to perform the above-mentionedcomputer-implemented method in a distributive manner.

The foregoing and other implementations can each optionally include oneor more of the following features, alone or in combination:

In some aspects, the protocol stack is a three-layer protocol stack thatincludes a physical layer, a data link layer, and a network layer.

In some aspects, where identifying a communication function for eachlayer of a protocol stack comprises: identifying a direct sequence codedivision multiple access (DS-CDMA) scheme as a multiple access schemefor the data link layer; and identifying a geographic routing algorithmas a routing scheme for the network layer.

In some aspects, where solving the optimization problem comprises one ormore of: performing a distributed power control based on anon-cooperative game theory; evaluating a relation between a chaoticcode of the DS-CDMA scheme and a link throughput for the data link layerof the protocol stack; or identifying a forwarder for a transmitter of atransceiver coil pair according to the geographic routing algorithm byperforming a two-phase decision process.

In some aspects, where the plurality of environment-dependent parameterscomprises a temperature, an electrical conductivity, a magneticpermeability, and a coil resistance.

In some aspects, the method further comprises evaluating effects of theplurality of environment-dependent parameters on quality of the MIcommunications among the plurality of sensors in the underground region.

In some aspects, where the transmission parameters comprise one or moreof a modulation scheme, a coding scheme, a transmitted power level, adata rate, a coding length of chaotic code, or a next-hop packetforwarder.

In some aspects, where the optimization problem further comprises one ormore communication constraints based on a quality of servicerequirement, a power control requirement, or both.

In some aspects, where the optimization problem comprises amulti-objective optimization problem, an application-driven optimizationproblem, or a combination of a multi-objective optimization problem andan application-driven optimization problem by a weight sum.

In some aspects, the optimization problem includes one or moreoptimization objectives that comprise minimum energy consumption,maximum network throughput, or both.

In some aspects, the method further comprises performing a random accessscheme when the transmitting of the signals fails.

Units of Measurements:

m Meter cm Centimeter S/m Siemens per meter H/m Henry per meter Ω/m Ohmper meter ° K Kelvin Hz Hertz

While generally described as computer-implemented software embodied ontangible media that processes and transforms the respective data, someor all of the aspects may be computer-implemented methods or furtherincluded in respective systems or other devices for performing thisdescribed functionality. The details of these and other aspects andimplementations of the present disclosure are set forth in theaccompanying drawings and the description in the following. Otherfeatures and advantages of the disclosure will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing an example protocol stack ofenvironment-aware cross-layer protocol design.

FIG. 2 is a plot showing an example network topology of multi-hoptransportation for WUSNs in underground oil reservoirs.

FIG. 3 is a plot showing an example transformer model and the equivalentcircuit of the example transceiver coil pair i-j shown in FIG. 2.

FIG. 4 is a flow chart of an example process of underground oilreservoir environment evaluation.

FIG. 5 is a flow chart of an example process of three layer protocolstack for WUSNs in underground oil reservoirs.

FIG. 6 is a diagram showing an example cross-layer framework for WUSNsin underground oil reservoirs.

FIG. 7 is a diagram showing an example Distributed Environment-AwareProtocol (DEAP) design for solving cross-layer framework in undergroundoil reservoirs.

FIG. 8 is a plot showing example frequency response for available 3-dBbandwidth of Magnetic Induction (MI)-based communications with differenttransmission ranges, working temperatures, and alignment angles oftransceiver coils.

FIG. 9 is a plot showing example effects of temperature and coildirection on path loss of MI-based communications.

FIG. 10 is a plot showing example effects of typical undergroundmodulation and channel coding on packet error rate (PER) of MIcommunications in WSUNs.

FIG. 11 is a table showing example parameter setup for comparisonbetween the example cross-layer framework and layered BPSK/No FEC schemeunder various environmental conditions in underground oil reservoirenvironment.

FIG. 12 is a plot showing example energy consumptions of the exampleframework cross-layer and the layered BPSK/No FEC scheme under differentenvironmental conditions.

FIG. 13 is a plot showing example average bit rates of the examplecross-layer framework and the layered BPSK/No FEC scheme under differentenvironmental conditions.

FIG. 14 is a table showing example performance evaluation of the exampleframework cross-layer under sea water condition in underground oilreservoirs.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This disclosure describes computer-implemented methods, software, andsystems for providing communication protocol architecture or frameworkfor magnetic induction (MI)-based communications in wireless undergroundsensor networks (WUSNs), for example, in underground oil reservoirs.

Underground environments create significant challenges for wirelesscommunication via classical electromagnetic (EM) waves. For example, themain problems of EM communication arise from extremely shortcommunication ranges, highly unreliable channel conditions, and largeantenna sizes, thus making them impractical for actual deployments ofWUSNs.

The magnetic induction (MI) technique is a promising alternativewireless communication solution to handle the underground challenges.The MI technique utilizes the near magnetic field of coils to propagatethe information, thus achieving constant channel conditions viasmall-size coils and making the MI communication suitable forunderground environments.

In some implementations, instead of taking the classical layeredprotocol approach (for example, the 7-layer Open Systems Interconnectionmodel (OSI Model)), a cross-layer framework is proposed for WUSNs inunderground oil reservoirs. For example, the cross-layer framework caninclude a three-layer protocol stack. In some implementations, thecross-layer framework can be implemented in a distributed manner tooptimize MI communication in WUSNs, for example, to jointly optimize thecommunication functionalities of different layers. DistributedEnvironment-Aware Protocol (DEAP) framework is an example distributedcross-layer protocol framework. Furthermore, a DEAP process is providedto solve the proposed cross-layer framework.

In some implementations, the distributed cross-layer framework such asDEAP framework can account for environment information of undergroundoil reservoirs that affects the transmission qualities of MI-basedcommunications. For example, MI channel models are developed thatcapture the physical layer functionalities. The effects of temperature,electrical conductivity, magnetic permeability, and coil resistance onthe MI-based communication (for example, the path loss, the bandwidth,and the interference) are studied and accounted for in distributedcross-layer protocol framework. Furthermore, the communicationfunctionalities, such as modulation and forward error coding, mediumaccess control (MAC), routing algorithms, and the statistical quality ofservice (QoS, for example, packet delay and transmission reliability)guarantees and their impacts on MI-based communications are studied. Theinteractions of key underground communication functionalities as well asthe QoS requirements of applications are investigated and taken intoconsideration of the distributed cross-layer protocol framework.

In some implementations, the cross-layer framework can provide solutionsto various performance requirements (for example, QoS requirements) ofdiverse applications. In some implementations, the Pareto optimality canbe considered for two-objective functions such as energy consumption andpacket transmitted rate. The weighted sum method further can convert theoptimization into a single objective problem via the specific weightvectors of applications. A distributed power control via anon-cooperative game is designed and a direct sequence code divisionmultiple access (DS-CDMA) scheme can be employed via a chaotic code anda geographical routing algorithm can be utilized by a two-phase decisionstrategy.

The example techniques can achieve one or more advantages. For example,the cross-layer framework can integrate these functionalities for theefficient utilization of the bandwidth-limited MI communicationchannels. The DEAP process can follow the cross-layer framework in adistributed manner, delivering statistical QoS guarantee and obtainingboth optimal energy savings and throughput gain concurrently forpractical implementation. The cross-layer framework can fulfill apre-defined level of QoS and take into account interactions of differentlayers' functionalities. The cross-layer framework can provide efficientresource utilization and achieve high system performances such as highenergy savings and throughput gain with low computation complexity. Thecross-layer framework can serve as a foundation for other designs ofdistributed cross-layer scheme in WUSNs. The example techniques can beapplicable to general wireless underground applications with variousrequirements in different communication layers due to the supportedreliable and efficient MI communications. In some applications, theexample techniques can achieve additional or different advantages.

Simulation results show that cross-layer framework outperforms thelayered protocol solutions with 50% energy savings and 6 dB throughputgain. Moreover, beyond the centralized cross-layer designs, thedistributed cross-layer framework includes two-phase-per-node-baseddecisions that requires only one-hop neighbor information, and has lowcomputation complexity. Thus, the distributed cross-layer design for MIcommunication in WUSNs establishes reliable and efficient transmissionsin challenged underground environments.

FIG. 1 is a diagram showing an example architecture 100 of a cross-layerprotocol framework. The cross-layer framework architecture 100 includesevaluations of underground oil reservoir environment 110. A three-layerprotocol stack 120 is proposed for WUSNs in the underground oilreservoir environment 110. The three-layer protocol stack 120 can modelMI-based communications in the underground oil reservoir environment 110with environment-aware functionalities of each protocol layer. Thethree-layer protocol stack 120 includes a physical layer (PHY) 122, adata link layer (DLL) 124, and a network layer (NET) 126.Functionalities of each layer can be evaluated to design, improve,optimize, or otherwise manage MI-based communications. For example,several modulation schemes such as BPSK, QPSK, and BFSK can be adoptedto reduce energy consumption. Channel coding like the multilevel cyclicBCH code can be considered. Moreover, a Direct-Sequence CDMA (DS-CDMA)can be designed and used to simplify performance degradation caused byinterference signals.

An environment-aware cross-layer framework 130 can take into account theenvironment information from the underground oil reservoir environment110, and can capture functionalities of each protocol layer of thethree-layer protocol stack 120. A DEAP process 140 can be performed tosolve the optimization problem of the framework in a distributed manner.As such, the cross-layer framework architecture 100 shows a coherentcross-layer framework is provided that can accurately model the layerednetwork architecture and integrate different communicationfunctionalities into a coherent framework, and provide cross-layerresource allocation and distributed cross-layer solutions.

For example, to achieve efficient MI communication in the undergroundenvironment, a distributed optimization problem can be performed tojointly control the physical, MAC and routing functionalities in thephysical layer (PHY) 122, data link layer (DLL) 124, and network layer(NET) 126, respectively. Suitable or optimal selection of modulation,FEC, and transmit power (physical functionalities), a DS-CDMA mediumaccess control scheme with power control constraint to access thebandwidth-limited MI channels (MAC functionality), and a geographicalrouting algorithm (routing functionality). Furthermore, the cross-layerDEAP solution is environmental-aware in capturing the underground MIchannels and can provide high utilization and achieve low energyconsumptions and limited computational complexity in WUSNs.

FIG. 2 is a plot showing an example system model 200 of MI-basedcommunication for WUSNs in underground oil reservoir 205. In someimplementations, a well system 215 can be implemented on the land in asubterranean region, for example, to perform fracture treatments inorder to produce oil and gas from underground oil reservoir 205. Aborehole 225 can be formed beneath the land surface and fractures 235can be formed in the underground oil reservoir 205.

Multiple miniaturized sensors (e.g., sensors S, Rs, i, j, and D) can beplaced in the underground oil reservoir 205 that form one or more WUSNsfor measuring conditions of the underground environment. The sensors canmeasure temperature, pressure, local fluid composition, chemicalcompositions, or other environment information of the underground oilreservoir 205. Some or all of environment information, as well as thesensor location information, can be communicated over the WUSN among themultiple sensors, for example, based on MI communications. In someimplementations, one or more or all of the sensors can be placed orinjected in deep soil regions at depths of substantially 6,000 feet (forexample, substantially 2.0 kilometers). The sensors can be placed in thedeep soil, for example, by entraining the sensors in a fluid and flowingthe fluid into the rock formation, for example, to a natural void in therock (such as a lost circulation zone) or a new void (such as ahydraulic fracture).

An MI communication link can be formed by the induction between aprimary coil and a secondary coil, as an alternating current exists inthe primary coil. In some implementations, each sensor in the WUSN caninclude, be attached to, or otherwise be associated with a coil as anantenna for MI communication. For example, a sensor can be an integratedsensor that has an embedded coil antenna or a sensor with external(attached) coils. Each sensor can include memory, a processor, or othercomputer-readable media or data processing apparatus operable to performthe example techniques for providing communication protocol architectureor framework for MI-based communications in the WUSN. For example, thesensors can include memory and processors for performing the exampleprocesses 400, 500, 600, and 600 in a distributive manner. In someimplementations, the sensors in the WUSN can include communicationinterfaces for establishing communications (e.g., radio frequencycommunications or Bluetooth communications) with a computer system ofthe well system 215. The MI communication link can be implemented tocommunicate at low frequencies (for example, tens of megahertz (MHz) orlower, such as, 7 MHz) using small size coils as antennas to increasestability in the channel condition for good transmission quality. Thecomputer system can be located near the underground oil reservoir 205 orremotely in a computing center or facility. In some implementations,some or all of the example techniques described in this disclosure (forexample, the example processes 400, 500, 600, and 600 can be implementedby the computer system in a centralized manner.

The MI communications can include single-hop and multi-hoptransportations. For example, an end-to-end MI transmission can includemore than two coils along the transmission route. FIG. 2 shows anexample multi-path data transportation 201 that includes a source sensor(S) one or more relay sensors (Rs), and a destination sensor (D). Ssends the data, Rs relay the data through multiple single-hoptransmissions, and D receives the data for further data processing. Thetwo coils 220 and 230 are an example single transceiver pair.

FIG. 2 also shows an example single-hop transmission between two sensorsi and j, where the sensor i is the transmitter and sensor j is thereceiver. The sensors i and j include a transmitter coil i 220 and areceiver coil j 230, respectively, forming a transceiver coil pair i-j210. The coils 220 and 230 can have the same radius a [cm] and number ofturns N in this example. The primary coil and secondary coil can havedifferent radius and number of turns in other instances. r [m] is thedistance between the transmitter coil 220 and the receiver coil 230 andΘ is the angle between the axes of two coupled coils 220 and 230.

In some instances, when sensor i transmits the signal, not only thetarget receiver j will receive the signal but also other sensors, suchas, sensors 1, . . . , sensor Ξ_(i) ^(T), may receive the interferencefrom sensor i. Similarly, when sensor j receives the signal, it mayreceive not only the desired signal from transmitter i but also theinterference from other transmitters, such as, sensor 1, . . . , sensorν_(j) ^(R). The directions of arrows 202 and 204 show the directions ofsingle-hop transmissions.

In some implementations, the constant channel conditions of small-sizecoils make MI communication suitable for underground environments. Theseenvironment parameters can affect the communication qualities ofmagnetic induction, which is used by each transmitter-receiver pair forMI communications. For example, the temperature can drive the MI-basedcommunications. The local fluid composition correlates withconductivity, dielectric constant, or both. The sensor locations can beused for mapping the fractures 235 of the underground oil reservoirs205. These environment parameters' impact on the MI-based communicationcan be evaluated and used to provide environment-aware cross-layerframework (for example, framework 130) for the WUSN.

FIG. 3 is a diagram showing an example system model 300 and theequivalent circuit 350 of the example transceiver coil pair i-j 210 inFIG. 2. The system model 300 can model physical magnetic inductiontransmissions for underground oil reservoirs. As shown in the examplesystem model 300, the MI transceiver 210 can be modeled as twotransformers 310 and 320, where V_(S) is the voltage of transmitter'sbattery, Z_(L), is the receiver's load impedance, M is the mutualinduction between two coils 220 and 230, R is the resistance of coppercoil, H is the self-induction of magnetic antenna (that is, coil), and Cis the loaded capacitor to guarantee resonance. Furthermore, theequivalent circuit of such a model can also obtained as

Z=R+j ωH+1/(j ωC),

Z _(rflt)=ω² M ²/(Z+Z _(L));

V_(M)=j ωMi_(t),

where ω is the angle frequency of the transmitting signal and it is thecurrent of transmitter's circuit.

FIG. 4 is a diagram of an example process 400 of underground oilreservoir environment evaluation. In some implementations, undergroundoil reservoir environment evaluation includes measurements of thetemperature, electrical conductivity, magnetic permeability, coilresistance or other environment conditions or parameters of a WUSN in anunderground oil reservoir, and evaluation of the path-loss and bandwidthfor the MI-based communication.

In some implementations, to fit the environment-aware design forunderground oil reservoirs, at 410, the operating temperature T andelectrical conductivity of medium a are measured, for example, by eachsensor in the WUSN. The temperature refers to the temperature of aspecific location where the sensor positioned. It can be used tocharacterize the communication quality of MI-based communications.

At 420, magnetic permeability of medium and coil resistance can becalculated based on the environment parameters. For example, themagnetic permeability and the coil resistance can be calculated asfollows:

$\mu = {{\mu_{0}( {1 + \chi} )} = {\mu_{0}( {1 + {p_{para}\frac{\hat{c}}{T}} + {p_{ferro}\chi_{ferro}}} )}}$R = 2π aNR₀[1 + a c_(u)(T − T₀)]

where μ₀ is the permeability of air, that is, μ₀=4π*10⁻⁷ [H/m], χ andχ_(f erro) are the magnetic susceptibilities of the medium andferromagnetic contents, p_(para) and p_(f erro) are the ratio ofparamagnetic and ferromagnetic composites, ĉ is the constant,α_(Cu)=3.9*10⁻³ [/° K] is the temperature coefficient of copper coil, T[° K] is the actual underground temperature and T₀[° K] and R₀ [Ω/m] arethe room temperature and the corresponding resistance of a unit lengthof coil.

At 430, the path loss and available transmission bandwidth can becalculated or otherwise evaluated based on the operating temperature Tand electrical conductivity of medium a, magnetic permeability of mediumand coil resistance. For example, according to circuit theory with theexample coil model 300 in FIG. 3, the path loss can be obtained as

${{L_{MI}( {r,f_{0},\theta,T,\sigma} )}\lbrack{dB}\rbrack} = {10\lg \; \frac{2( {{2R^{2}} + {\omega_{0}^{2}M^{2}}} )}{\omega_{0}^{2}M^{2}}}$

where f₀ [MHz]=ω₀/2π is the frequency of transmitted signal and M is themutual induction of transmitter's and receiver's coils. The 3-dBbandwidth B_(MI) [KHz] is adopted as the MI channel bandwidth.Specifically, the path loss at f₀+0.5B_(MI) can be twice of that at f₀.Since the channel bandwidth is much smaller than the central frequency(that is, f₀+0.5B_(MI)≈f₀), the approximated bandwidth can thus beobtained as

$B_{MI} = {\frac{R( {\sqrt{2} - 1} )}{\mu \; \pi^{2}{aN}^{2}}.}$

In some implementations, the path loss and available transmissionbandwidth can be evaluated under different parameter settings as theseenvironment-dependent parameters affect the transmission quality ofMI-based communication.

In some instances, the environment parameters vary over time, thesensors can measure the temperature periodically, continuously, fromtime to time, or in real time. In some implementations, theenvironment-aware cross layer protocol frame can make decision based onthese environment-dependent parameters and update over time based on thechanges of the sensed parameters.

FIG. 5 is a diagram showing of an example environment-aware protocolstack 500 for WUSNs in underground oil reservoirs process. The exampleenvironment-aware protocol stack 500 is a three-layer protocol stack,rather than conventional seven-layer stacks (for example, the OSI model)to save the energy consumption due to protocol processing. Thethree-layer protocol stack 500 includes a physical layer (PHY) 510, adata link layer (DLL) 520, and a network layer (NET) 530.

In some implementations, the PHY 510 and DLL 520 are necessary forsuccessful communications of each transmitter-receiver pair (that is,point-to-point communications) in underground environments. The NET 530is responsible for packet transmission from source to destination (thatis, end-to-end communications), for example, with routing algorithms.The three-layer protocol stack 500 can reduce the implementation andcomputational costs of sophisticated design of higher layers of the OSImodel (for example, Transport Layer, Session Layer, Presentation Layer,Application Layer).

For each layer, the communication functions are built with theconsideration of the environment information. The communicationfunctions of PHY 510 include, for example, modulation, channel coding(for example, forward error correction (FEC)), and transmitted powerallocation. The communication functions of DLL 520 include, for example,multiple accesses according to certain multiple access schemes (forexample, DS-CDMA or other multiple access schemes). The communicationfunctions of NET 530 include, for example, routing packets according tocertain routing algorithms (for example, geographical routing algorithmor other algorithms).

In some instances, the DS-CDMA scheme that uses chaotic codes cancompensate the drawbacks of multi-path effects and achieve high channelreuse as well as low number of packet retransmissions. Thus, itdecreases the energy consumption and increases the network throughput.As chaotic codes provide much higher granularity with less constraint incode properties than the pseudo-random sequences, the chaotic code withlength l_(ij) [bit] ∈ L=[l_(min), l_(max)] can be adopted fortransmissions over link i-j.

While DS-CDMA allows multiple transmitters to transmit at the same time,the transmitter near the receiver will have higher received power levelthan the one far from the receiver. This causes the near-far problem ifthe near transmitter is not the target sender of the receiver and willinduce high interference for the transmissions from the targetsender/transmitter. To implement DS-CDMA in WUSNs for underground oilreservoirs, the near-far effect needs to be addressed, for example, tomake signals arrived at the receiver to have approximately the same meanpower. In some instances, the interferences from simultaneoustransmissions of different users need to be controlled. In someimplementations, these requirements can be satisfied by proper powercontrol design.

In some implementations, for the network layer, a geographical routingalgorithm can be used because of its scalability feature and limitedsignaling overhead requirements. The geographical routing algorithm is adistributed algorithm. A source or relay node i can select its best nexthop j* among the set of possible forwarders given an objective function,such as an energy consumption threshold, an achievable throughput, or acombination of these and other objectives.

In some implementations, with the built communication functions for eachlayer, communication constraints (also referred to as constraints ofcommunication functions) 540 can be determined based on requiredquality-of-service (QoS) and power control. The communicationconstraints can include, for example, power control constraint,reliability constraint, average delay constraint, and delay varianceconstraint.

FIG. 6 is a diagram showing an example cross-layer framework 600 forWUSNs in underground oil reservoirs. The example cross-layer framework600 can include multi-objective optimizations 610 and application-drivenobjective optimization 620 to be performed individually, sequentially,or in parallel.

In some implementations, the multi-objective optimizations 610 caninclude a two-objective optimization with respect to the energyconsumption and the network throughput, which are the two key factorsfor the energy-efficient and reliable communications. For example, themulti-objective optimizations 610 can aim for both optimal energyconsumption and system throughput. In some implementations, formulti-objective optimizations, the Pareto optimal front can provide thebest solution sets that include non-dominated solutions, and thus givesthe performance benchmarks. The Pareto optimal front is the line thatshows all possible values of studied parameters can be achieved at thesame time. In this example two-objective optimization, the Paretooptimal front describes a 2-tuple parameter, (achievable throughput,consumed energy). When the consumed energy increases or decreases, theachievable throughput will increase or decrease, respectively.

On the other hand, application-oriented WUSNs usually have their ownpurposes, such as sensing the scalar data in harsh environments ormonitoring the objects with high data rate requirements accompanied bypossible build-in energy harvesting. In some implementations, theapplication-driven objective optimization 620 can be jointly consideredwith the multi-objective optimizations 610, for example, via a weightedsum method. The weighted sum method can apply a weight vector tomultiple applications that are considered at the same time. For example,the multi-objective optimization framework 610 can be set with theobjectives of energy minimization and throughput maximization, subjectto the obtained communication constraints determined at 540. To minimizeenergy consumption E and maximize throughput Q at the same time, theweighted sum method can be applied to minimize w₁E−w₂Q, where w=[w₁ w₂]is a weight vector that can be pre-determined according to thepreference of energy or throughput performance. As such, themulti-objective framework 610 is converted to application-drivenobjective optimization 620 through the weight vector that optimizesenergy consumption and achievable throughput at the same time.Additional or different optimization objectives can be determined, forexample, by a user's application of WUSN before deploying the sensors.In some implementations, the user's applications determine the weightvectors and the weighted sum method can be applied according to weightvectors, transforming multi-objective optimization to single-objectiveoptimization.

In some implementations, the optimizations (for example, theoptimization 610, 620, or both) can be achieved by adjusting thecorresponding transmission parameters based on the environment-dependentparameters. In some implementations, adjusting the transmissionparameters can include updating or otherwise determining values or modesof the transmission parameters to achieve certain objectives (such as amaximum throughput and a minimum energy consumption). The transmissionparameters can be determined by solving an optimization problem. Forexample, the transmission parameters can be determined by solving anoptimization problem with the identified multiple objectives and subjectto the communication constraints. The transmission parameters include,for example, modulation and coding schemes, applied power level,achievable data rates, coding length of a chaotic code for DS-CDMA, andthe next-hop packet forwarder. The cross-layer framework 600 enables theadjustment of transmission parameters based on environment-dependentparameters (for example, magnetic permeability and coil resistance,particularly the measured temperature and electrical conductivity).

In some implementations, in the proposed cross-layer framework, thetransmission parameters can be determined for each transmitter-receiverpair (for example, the transceiver coil pair i-j 210) before thetransmitter transmits its packets. In some implementations, thetransmission parameters can be determined based on the real-time valuesof environment-dependent parameters. In this way, the communicationquality can be estimated and optimized dynamically, for example, beforethe actual transmissions take place.

In some implementations, the optimizations (for example, theoptimization 610, 620, or both) can be implemented as a software modulethat decides the best transmission parameters among the communicationsof underground sensors. The software module can be implemented in eachof the sensors in the WUSNs. In some implementations, the softwaremodule can be implemented in one or more computer system in addition tothe sensors in the WUSNs.

FIG. 7 is a diagram showing an example Distributed Environment-AwareProtocol (DEAP) process 700 for solving a cross-layer framework inunderground oil reservoirs. In some implementations, the DEAP process700 can be performed to solve the application-driven objectiveoptimization 620 of the cross-layer framework 600. In someimplementations, the DEAP process 700 can be performed on a per node orsensor basis. The DEAP process 700 can include distributed power control710, relation evaluation between a chaotic code and a link throughput720, and two-phase decision strategy 730 for the actual transmissionbetween each transmitter-receiver pair.

The DEAP process 700 can be performed after a sensing and informationexchange phase 705 where the environmental information (for example, thefour environment-dependent parameters 630) is collected and determined,for example, based on measurements from sensors in the WUSN. The sensingand information exchange phase 705 can be similar to or different formthe example process 400 of underground oil reservoir environmentevaluation.

The distributed power control 710 can solve the near-far problem of theDS-CDMA scheme, for example, based on the non-cooperative game theory.In other words, the distributed power control 710 can be formulated as agame problem and solved with the aid of game theory techniques. As anexample, the inputs to the game problem can include utility functions ofa sensor, such as:

u _(i)(p _(i) , p _(−i))=R _(i)(p _(i) , p _(−i))−C _(i)(p _(i) , p_(−i))

where strategy p_(i) of sensor i represents the transmitted power level,strategy p_(−i) represents other sensors' power levels, the revenuefunction R_(i) represents the achievable data rate that relates toreceived signal to noise and interference ratio (SINR) level, and thecost function C_(i) represents the instantaneous price that sensor ipays for using a specific amount of power that causes interference toother ongoing transmissions.

In some implementations, the game theory can be applied to yield anoptimal solution in a distributed manner. For example, the game theoryproblem can be solved by each sensor individually for its owntransmitted power. The existence and the uniqueness of Nash Equilibriumin this distributed power control game can be examined that derives thebest suitable power level for each sensor. The derived power level canthen be programmed inside sensors as power control policies.

The relation evaluation 720 facilitates the implementations of thecross-layer framework in a distributed manner. There is no signalingneeded to optimize the transmission parameters and each sensor candecide those parameters locally. The relation evaluation 720 can assessthe relation between a chaotic code and a link throughput, for example,when applying DS-CDMA for DLL layer according to the example techniquesdescribed with respect to FIG. 5. In some implementations, the relationbetween a chaotic code and a link throughput can be characterized by afunction whose output is a link throughput and whose inputs have achaotic code length. For example, the link throughput Q_(ij) can bemodeled as:

Q _(ij) =f(l _(ij) , T, σ, . . . ),

where l_(ij) denotes code length, T temperature, and a electricalconductivity.

In some implementations, a two-phase decision procedure 730 can beperformed to enable the favored geographical routing to leverage itsscalability and limited signaling overhead, which is well-suited forunderground oil reservoir environment. The two-phase decision procedure730 decides the optimal transmission parameters for all possibleforwarders and then determines the optimal forwarder, for example, whichprovides the optimal energy consumption and achievable throughput. Morespecifically, the two phases include a link quality examination phaseand a next-hop selection phase. For the link quality examination phase,the transmit power level, the allowed link throughput, the code lengthof DS-CDMA can be optimized through processes 710 and 720 with respectto all feasible next-hop neighbors and all possible combinations ofmodulation and channeling coding functionalities. FIG. 10 lists a set ofpossible functionalities. Then, for next-hop selection phase, the bestnext-hop and the functionalities associated with the best link qualityresult can be selected. Actual transmissions can take place between theselected transmitter-receiver pair.

In some implementations, random access scheme 740 can be performedwhenever the actual transmission is failed. The random access scheme 740can include a retransmission after some random or specific waiting time.The random access scheme 740 supplements DEAP and guarantees successfultransmissions between transmitter sensor and receiver sensor. Severalwell-known random access schemes can be applied here, such as ALOHA,slotted-ALOHA, Carrier sense multiple access (CSMA), etc. The randomaccess scheme 740 can be performed to guarantee the reliablecommunications for loss-tolerance purposes.

Simulations have been performed to evaluate the performance of theexample cross-layer protocol framework. In particular, the MI-basedchannel modeling is first evaluated through the simulations on 3-dBbandwidth and path loss, with respect to different temperatures and coildirections. Furthermore, the effects of typical underground modulationand channel coding on packet error rate (PER) are evaluated based on thebuilt channel modeling. A comprehensive evaluation of the examplecross-layer communication protocol framework, that is, the DEAPframework, is conducted with practical setting of system parameters inunderground oil reservoirs. Remarkable energy savings and highthroughput gain have been shown. The DEAP framework can be favored bypractical implementations in WUSNs.

To provide satisfactory cross-layer design for practical underground oilreservoir, the cross-layer framework is able to optimize thecorresponding transmission parameters by capturing the unique impacts ofenvironment parameters, which can be shown in FIGS. 8 and 9 below,respectively.

FIG. 8 is a plot 800 showing example frequency responses of MI channelsin the oil reservoir environment. The plot 800 shows path losses (in dB)of MI-based communications with different transmission ranges, workingtemperatures, and alignment angles of transceiver coils. It is shownthat the 3-dB bandwidth is around 1 KHz and is not affected by thetransmission range and the alignment angles.

FIG. 9 is a plot 900 showing example effects of temperature and coildirection on path loss of MI-based communications. The plot 800 showspath losses (in dB) of a single transmitter-receiver pair with respectto the transmission distance under different temperatures and coildirections. As the temperature goes higher, the path loss increases andthus degrades the link transmission qualities. The cross-layer frameworkcan capture such environment-dependent bandwidth and path loss behaviorbecause, in the cross-layer protocol framework, communication parameterswill be adjusted according to the changes of both link data rate andSINR value, which are the functions of environment parameters ofunderground oil reservoir.

MI communications enjoy less channel variations than EM waves. Theundesired noise, mainly from the thermal vibration of circuit elements,justifies its modeling as additive white Gaussian noise. The modulationand channel coding functionalities are analyzed in view of thecharacteristics of MI communications. For example, due to energy-limitedWUSNs, more sophisticated modulation techniques bring much energyconsumption. Several simple and suitable modulation schemes, such asBPSK, BFSK, DBPSK, and 16-QAM, can be selected for the wirelessunderground communications. On the other hand, regarding the channelcoding schemes, forward error correction (FEC) enhances the linktransmission reliability without additional re-transmission cost andoverhead, while the automatic repeat request (ARQ) does. Furthermore,along with high energy efficiency, block codes have lower complexity ascompared to convolutional codes (CC). In particular, a multilevel cyclicBCH (Bose, Ray-Chaudhuri, Hocquenghem) code outperforms the mostenergy-efficient CC by almost 15% for the optimal packet size inwireless sensor networks. Thus, BCH code can be used in the cross-layerprotocol framework; however, the cross-layer framework can be extendedto support additional or different energy-consuming schemes, such asReed-Solomon codes, CCs, turbo codes as well as different types of ARQ.

FIG. 10 is a plot 1000 showing example PER curves of differentcombinations of modulation and channel coding schemes, showing effectsof modulation and channel coding on PER of MI communications in WSUNs.

FIG. 11 is a table 1100 showing example parameter setup for comparisonbetween proposed cross-layer DEAP framework and the layered BPSK/No FECscheme under various environmental conditions in underground oilreservoir environment.

FIG. 12 is a plot 1200 showing example energy consumption of the DEAPframework and the layered BPSK/No FEC scheme under differentenvironmental conditions. FIG. 13 is a plot 1300 showing example averagebit rates of the DEAP framework and the layered BPSK/No FEC scheme underdifferent environmental conditions. As the temperature and electricalconductivity increase, the energy consumption increases and theachievable bit rate decreases. The DEAP framework outperforms the otherscheme in all kinds of environmental conditions. In particular, whileBPSK/No FEC scheme cannot work at 500° C. for all kinds of mediumconductivities, the DEAP framework still functions well in suchscenarios.

The DEAP framework is further evaluated for the underground environmentwith very high medium conductivity (for example, 4.8 [S/m] sea water atroom temperature 20° C.). The detailed results are shown in Table 1400in FIG. 14. Table 1400 shows example performance evaluation of the DEAPframework under sea water condition in underground oil reservoirs, wheretransmission range is set as 0.68 [m] with perfect alignment of coilantenna. Such a scenario causes dramatic degradation for the MI-basedtransmission quality, as the basic MI concept is to exploit the signalinduction. Only DEAP framework can work in this harsh condition for thevery short transmission range (0.68 [m] in this case). Furthermore, withtemperature increases for sea water condition, the energy consumptiondecreases and the achievable bit rate increases, which provide bettersystem performance. The opposite effect due to temperature differencescomes from the reason that the impact of temperature increasing becomesnon-negligible anymore, regarding such a high conductivity value at roomtemperature.

The operations described in this disclosure can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources. The term “data processing apparatus” encompasses all kinds ofapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, a system on a chip, ormultiple ones, or combinations, of the foregoing. The apparatus caninclude special purpose logic circuitry, for example, an FPGA (fieldprogrammable gate array) or an ASIC (application-specific integratedcircuit). The apparatus can also include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, for example, code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (for example, one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (for example, files that store one or moremodules, sub-programs, or portions of code). A computer program can bedeployed to be executed on one computer or on multiple computers thatare located at one site or distributed across multiple sites andinterconnected by a communication network.

While this disclosure contains many specific implementation details,these should not be construed as limitations on the scope of anyimplementations or of what may be claimed, but rather as descriptions offeatures specific to particular implementations. Certain features thatare described in this disclosure in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable subcombination. Moreover,although features may be described previously as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described previously should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

1. A method comprising: evaluating environment information of anunderground region that affects the transmission qualities of magneticinduction (MI) communications; identifying a protocol stack including aplurality of layers for MI communications among a plurality of sensorsin a wireless underground sensor network (WUSN) in the undergroundregion; and building a cross-layer framework to jointly optimizecommunication functionalities of the plurality of layers based on theevaluation.
 2. The method of claim 1, wherein the protocol stack is athree-layer protocol stack that includes a physical layer, a data linklayer, and a network layer.
 3. The method of claim 1, further comprisingsolving the cross-layer framework by an environment-aware protocol(DEAP) process based on the evaluation.
 4. The method of claim 3,wherein the DEAP process comprises one or more of: a distributed powercontrol; an evaluation of a multiple access scheme for a data link layerof the protocol stack; or a two-phase decision process for performing arouting algorithm for a network layer of the protocol stack.
 5. A methodcomprising: identifying, by each of a plurality of sensors in a wirelessunderground sensor network (WUSN) in an underground region, a pluralityof environment-dependent parameters measured by the plurality ofsensors; identifying, by each of the plurality of sensors, respectivecommunication functions for a plurality of layers of a protocol stackfor magnetic induction (MI) communications among the plurality ofsensors in the WUSN in the underground region; identifying, by each ofthe plurality of sensors, an optimization problem for jointly optimizingthe respective communication functions of the plurality of layers of theprotocol stack based on the plurality of environment-dependentparameters, the optimization problem including a plurality oftransmission parameters defining the respective communication functionsof the plurality of layers of the protocol stack; determining, by eachof the plurality of sensors, the plurality of transmission parameters bysolving the optimization problem; and transmitting, by each of theplurality of sensors based on magnetic induction, signals using theplurality of transmission parameters defining the respectivecommunication functions of the plurality of layers of the protocolstack.
 6. The method of claim 5, wherein the protocol stack is athree-layer protocol stack that includes a physical layer, a data linklayer, and a network layer.
 7. The method of claim 6, whereinidentifying a communication function for each layer of a protocol stackcomprises: identifying a direct sequence code division multiple access(DS-CDMA) scheme as a multiple access scheme for the data link layer;and identifying a geographic routing algorithm as a routing scheme forthe network layer.
 8. The method of claim 6, wherein solving theoptimization problem comprises one or more of: performing a distributedpower control based on a non-cooperative game theory; evaluating arelation between a chaotic code of the DS-CDMA scheme and a linkthroughput for the data link layer of the protocol stack; or identifyinga forwarder for a transmitter of a transceiver coil pair according tothe geographic routing algorithm by performing a two-phase decisionprocess.
 9. The method of claim 5, wherein the plurality ofenvironment-dependent parameters comprises a temperature, an electricalconductivity, a magnetic permeability, and a coil resistance.
 10. Themethod of claim 5, further comprising evaluating effects of theplurality of environment-dependent parameters on quality of the MIcommunications among the plurality of sensors in the underground region.11. The method of claim 5, wherein the transmission parameters comprisesone or more of a modulation scheme, a coding scheme, a transmitted powerlevel, a data rate, a coding length of chaotic code, or a next-hoppacket forwarder.
 12. The method of claim 5, wherein the optimizationproblem further comprises one or more communication constraints based ona quality of service requirement, a power control requirement, or both.13. The method of claim 5, wherein the optimization problem comprises amulti-objective optimization problem, an application-driven optimizationproblem, or a combination of a multi-objective optimization problem andan application-driven optimization problem by a weight sum.
 14. Themethod of claim 5, wherein the optimization problem comprises one ormore optimization objectives that comprise a minimum energy consumption,a maximum network throughput, or both.
 15. The method of claim 5,further comprising performing a random access scheme when thetransmitting of the signals fails.
 16. A non-transitorycomputer-readable medium storing instructions executable by a computersystem to perform operations comprising: evaluating environmentinformation of an underground region that affects the transmissionqualities of magnetic induction (MI) communications; identifying aprotocol stack including a plurality of layers for MI communicationsamong a plurality of sensors in a wireless underground sensor network(WUSN) in the underground region; and building a cross-layer frameworkto jointly optimize communication functionalities of the plurality oflayers based on the evaluation.
 17. The computer-readable medium ofclaim 16, wherein the protocol stack is a three-layer protocol stackthat includes a physical layer, a data link layer, and a network layer.18. The computer-readable medium of claim 16, wherein the operationsfurther comprise solving the cross-layer framework by anenvironment-aware protocol (DEAP) process based on the evaluation. 19.The computer-readable medium of claim 18, wherein the DEAP processcomprises one or more of: a distributed power control; an evaluation ofa multiple access scheme for a data link layer of the protocol stack; ora two-phase decision process for performing a routing algorithm for anetwork layer of the protocol stack.
 20. A system comprising one or morewireless sensors that include: memory; and data processing apparatusoperable to: evaluate environment information of an underground regionthat affects the transmission qualities of magnetic induction (MI)communications; identify a protocol stack including a plurality oflayers for MI communications among a plurality of sensors in a wirelessunderground sensor network (WUSN) in the underground region; and build across-layer framework to jointly optimize communication functionalitiesof the plurality of layers based on the evaluation.
 21. The system ofclaim 20, wherein the protocol stack is a three-layer protocol stackthat includes a physical layer, a data link layer, and a network layer.22. The system of claim 20, the data processing apparatus operable tosolve the cross-layer framework by an environment-aware protocol (DEAP)process based on the evaluation.
 23. The system of claim 22, wherein theDEAP process comprises one or more of: a distributed power control; anevaluation of a multiple access scheme for a data link layer of theprotocol stack; or a two-phase decision process for performing a routingalgorithm for a network layer of the protocol stack.